Scatter Plots

Haley Jeppson

2018-05-17

This document is adapted from the Bar Charts section of the Altair Example Gallery.

Our first step is to set up our environment:

# devtools::install_github("vegawidget/altair")
library("altair")
library("tibble")

vega_data <- import_vega_data()

Anscombe’s Quartet

Altair example

Data

glimpse(vega_data$anscombe())
#> Observations: 44
#> Variables: 3
#> $ Series <chr> "I", "I", "I", "I", "I", "I", "I", "I", "I", "I", "I", ...
#> $ X      <dbl> 10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5, 10, 8, 13, 9, 11,...
#> $ Y      <dbl> 8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, ...

Chart

chart <- 
  alt$Chart(r_to_py(vega_data$anscombe()))$
  mark_circle()$
  encode(
    x = alt$X("X", scale = alt$Scale(zero = FALSE)),
    y = alt$Y("Y", scale = alt$Scale(zero = FALSE)),
    column="Series"
  )$
  properties(
    width = 150,
    height = 150
  )

chart

Becker’s Barley Trellis Plot

Altair example

Data

glimpse(vega_data$barley())
#> Observations: 120
#> Variables: 4
#> $ site    <chr> "University Farm", "Waseca", "Morris", "Crookston", "G...
#> $ variety <chr> "Manchuria", "Manchuria", "Manchuria", "Manchuria", "M...
#> $ year    <dbl> 1931, 1931, 1931, 1931, 1931, 1931, 1931, 1931, 1931, ...
#> $ yield   <dbl> 27.00000, 48.86667, 27.43334, 39.93333, 32.96667, 28.9...

Chart

chart <- 
  alt$Chart(r_to_py(vega_data$barley()))$
  mark_point()$
  encode(
    x = alt$X("median(yield)", scale=alt$Scale(zero=FALSE)),
    y = alt$Y(
        "variety",
        sort = alt$SortField(
          field = "yield", 
          op = "median", 
          order = "descending"
        ),
        scale = alt$Scale(rangeStep = 20)
    ),
    color = "year:N",
    row = "site"
  )

chart

Binned Scatterplot

Altair example

Data

movies <- vega_data$movies()

glimpse(movies)
#> Observations: 3,201
#> Variables: 16
#> $ Creative_Type          <list> [NULL, NULL, NULL, NULL, "Contemporary...
#> $ Director               <list> [NULL, NULL, NULL, NULL, NULL, NULL, "...
#> $ Distributor            <list> ["Gramercy", "Strand", "Lionsgate", "F...
#> $ IMDB_Rating            <dbl> 6.1, 6.9, 6.8, NaN, 3.4, NaN, 7.7, 3.8,...
#> $ IMDB_Votes             <dbl> 1071, 207, 865, NaN, 165, NaN, 15133, 3...
#> $ MPAA_Rating            <list> ["R", "R", NULL, NULL, "R", NULL, "R",...
#> $ Major_Genre            <list> [NULL, "Drama", "Comedy", "Comedy", "D...
#> $ Production_Budget      <dbl> 8000000, 300000, 250000, 300000, 100000...
#> $ Release_Date           <list> ["12-Jun-98", "7-Aug-98", "28-Aug-98",...
#> $ Rotten_Tomatoes_Rating <dbl> NaN, NaN, NaN, 13, 62, NaN, NaN, NaN, 2...
#> $ Running_Time_min       <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,...
#> $ Source                 <list> [NULL, NULL, NULL, NULL, "Original Scr...
#> $ Title                  <list> ["The Land Girls", "First Love, Last R...
#> $ US_DVD_Sales           <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,...
#> $ US_Gross               <dbl> 146083, 10876, 203134, 373615, 1009819,...
#> $ Worldwide_Gross        <dbl> 146083, 10876, 203134, 373615, 1087521,...

Chart

chart <- 
  alt$Chart(r_to_py(movies))$
  mark_circle()$
  encode(
    alt$X("IMDB_Rating:Q", bin = TRUE),
    alt$Y("Rotten_Tomatoes_Rating:Q", bin = TRUE),
    size="count(IMDB_Rating)"
  )

chart

Bubble Plot

Altair example

Data

glimpse(vega_data$cars())
#> Observations: 406
#> Variables: 9
#> $ Acceleration     <dbl> 12.0, 11.5, 11.0, 12.0, 10.5, 10.0, 9.0, 8.5,...
#> $ Cylinders        <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, ...
#> $ Displacement     <dbl> 307, 350, 318, 304, 302, 429, 454, 440, 455, ...
#> $ Horsepower       <dbl> 130, 165, 150, 150, 140, 198, 220, 215, 225, ...
#> $ Miles_per_Gallon <dbl> 18, 15, 18, 16, 17, 15, 14, 14, 14, 15, NaN, ...
#> $ Name             <chr> "chevrolet chevelle malibu", "buick skylark 3...
#> $ Origin           <chr> "USA", "USA", "USA", "USA", "USA", "USA", "US...
#> $ Weight_in_lbs    <dbl> 3504, 3693, 3436, 3433, 3449, 4341, 4354, 431...
#> $ Year             <dttm> 1970-01-01, 1970-01-01, 1970-01-01, 1970-01-...

Chart

chart <- 
  alt$Chart(r_to_py(vega_data$cars()))$
  mark_point()$
  encode(
    x = "Horsepower:Q",
    y = "Miles_per_Gallon:Q",
    size = "Acceleration"
  )

chart

Connected Scatterplot (Lines with Custom Paths)

Altair example

Data

glimpse(vega_data$driving())
#> Observations: 55
#> Variables: 4
#> $ gas   <dbl> 2.38, 2.40, 2.26, 2.31, 2.27, 2.25, 2.22, 2.12, 2.11, 2....
#> $ miles <dbl> 3675, 3706, 3766, 3905, 3935, 3977, 4085, 4218, 4369, 45...
#> $ side  <chr> "left", "right", "bottom", "top", "right", "bottom", "ri...
#> $ year  <dbl> 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 19...

Chart

lines <- 
  alt$Chart(r_to_py(vega_data$driving()))$
  mark_line()$
  encode(
    x = alt$X("miles", scale = alt$Scale(zero = FALSE)),
    y = alt$Y("gas", scale = alt$Scale(zero = FALSE)),
    order="year"
  )

points <- 
  alt$Chart(r_to_py(vega_data$driving()))$
  mark_circle()$
  encode(
    alt$X("miles", scale = alt$Scale(zero = FALSE)),
    alt$Y("gas", scale = alt$Scale(zero = FALSE))
  )

chart <- (lines + points)

chart

Dot Dash Plot

Altair example

Inspired by Edward Tufte’s Visual Display of Quantitative Information (p. 133), this is based on g3o2’s block. It is also interactive, you can make an interval selection on the scatterplot.

Data

glimpse(vega_data$cars())
#> Observations: 406
#> Variables: 9
#> $ Acceleration     <dbl> 12.0, 11.5, 11.0, 12.0, 10.5, 10.0, 9.0, 8.5,...
#> $ Cylinders        <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, ...
#> $ Displacement     <dbl> 307, 350, 318, 304, 302, 429, 454, 440, 455, ...
#> $ Horsepower       <dbl> 130, 165, 150, 150, 140, 198, 220, 215, 225, ...
#> $ Miles_per_Gallon <dbl> 18, 15, 18, 16, 17, 15, 14, 14, 14, 15, NaN, ...
#> $ Name             <chr> "chevrolet chevelle malibu", "buick skylark 3...
#> $ Origin           <chr> "USA", "USA", "USA", "USA", "USA", "USA", "US...
#> $ Weight_in_lbs    <dbl> 3504, 3693, 3436, 3433, 3449, 4341, 4354, 431...
#> $ Year             <dttm> 1970-01-01, 1970-01-01, 1970-01-01, 1970-01-...

Chart

cars <- r_to_py(vega_data$cars())

brush <- alt$selection(type = "interval")

tick_axis <- alt$Axis(labels=FALSE, domain=FALSE, ticks=FALSE)

tick_axis_notitle <- 
  alt$Axis(labels=FALSE, domain=FALSE, ticks=FALSE, title="")

points <-
  alt$Chart(cars)$
  mark_point()$
  encode(
    x = alt$X("Miles_per_Gallon", axis = alt$Axis(title = "")),
    y = alt$Y("Horsepower", axis = alt$Axis(title = "")),
    color = alt$condition(brush, "Origin", alt$value("grey"))
  )$
  properties(selection = brush)

x_ticks <- 
  alt$Chart(cars)$
  mark_tick()$
  encode(
    x = alt$X("Miles_per_Gallon", axis = tick_axis),
    y = alt$Y("Origin", axis = tick_axis_notitle),
    color = alt$condition(brush, "Origin", alt$value("lightgrey"))
  )$
  properties(selection = brush)

y_ticks <- 
  alt$Chart(cars)$
  mark_tick()$
  encode(
    alt$X("Origin", axis = tick_axis_notitle),
    alt$Y("Horsepower", axis = tick_axis),
    color=alt$condition(brush, "Origin", alt$value("lightgrey"))
  )$
  properties(selection = brush)

chart <- (y_ticks | (points & x_ticks))

chart

Gapminder Bubble Plot

Altair example

This example is modified from an example in Lisa Charlotte Rost’s blog post ‘One Chart, Twelve Charting Libraries’.

Data

glimpse(vega_data$gapminder_health_income())
#> Observations: 187
#> Variables: 4
#> $ country    <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "An...
#> $ income     <dbl> 1925, 10620, 13434, 46577, 7615, 21049, 17344, 7763...
#> $ health     <dbl> 57.63, 76.00, 76.50, 84.10, 61.00, 75.20, 76.20, 74...
#> $ population <dbl> 32526562, 2896679, 39666519, 70473, 25021974, 91818...

Chart

chart <- 
  alt$Chart(r_to_py(vega_data$gapminder_health_income()))$
  mark_circle()$
  encode(
    alt$X("income:Q", scale = alt$Scale(type = "log")), 
    alt$Y("health:Q", scale = alt$Scale(zero = FALSE)), 
    size="population:Q"
  )

chart

Multifeature Scatter Plot

Altair example

Data

glimpse(vega_data$iris())
#> Observations: 150
#> Variables: 5
#> $ petalLength <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, ...
#> $ petalWidth  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, ...
#> $ sepalLength <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, ...
#> $ sepalWidth  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, ...
#> $ species     <chr> "setosa", "setosa", "setosa", "setosa", "setosa", ...

Chart

chart <- 
  alt$Chart(r_to_py(vega_data$iris()))$
  mark_circle()$
  encode(
    x = alt$X("sepalLength", scale = alt$Scale(zero = FALSE)),
    y = alt$Y("sepalWidth", scale = alt$Scale(zero = FALSE, padding = 1)),
    color = "species",
    size = "petalWidth"
  )

chart

Natural Disasters

Altair example

Data

glimpse(vega_data$disasters())
#> Observations: 803
#> Variables: 3
#> $ Entity <chr> "All natural disasters", "All natural disasters", "All ...
#> $ Year   <dbl> 1900, 1901, 1902, 1903, 1905, 1906, 1907, 1908, 1909, 1...
#> $ Deaths <dbl> 1267360, 200018, 46037, 6506, 22758, 42970, 1325641, 75...

Chart

chart <- 
  alt$Chart(r_to_py(vega_data$disasters()))$
  mark_circle(opacity = 0.8, stroke = "black", strokeWidth = 1)$
  encode(
    x = alt$X("Year:O", axis = alt$Axis(labelAngle = 0)),
    y = alt$Y("Entity:N"),
    size = alt$Size(
      "Deaths:Q",
      scale = alt$Scale(range = c(0, 5000)),
      legend = alt$Legend(title = "Annual Global Deaths")
    ),
    color = alt$Color("Entity:N", legend = NULL)
   )$
  properties(
    width = 480,
    height = 350
  )$
  transform_filter("datum.Entity != 'All natural disasters'")

chart

Polynomial Fit Plot

Altair example

TODO: need to recreate this example, thinking about “tidy” workflow

Chart

Scatter Matrix

Altair example

Data

glimpse(vega_data$cars())
#> Observations: 406
#> Variables: 9
#> $ Acceleration     <dbl> 12.0, 11.5, 11.0, 12.0, 10.5, 10.0, 9.0, 8.5,...
#> $ Cylinders        <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, ...
#> $ Displacement     <dbl> 307, 350, 318, 304, 302, 429, 454, 440, 455, ...
#> $ Horsepower       <dbl> 130, 165, 150, 150, 140, 198, 220, 215, 225, ...
#> $ Miles_per_Gallon <dbl> 18, 15, 18, 16, 17, 15, 14, 14, 14, 15, NaN, ...
#> $ Name             <chr> "chevrolet chevelle malibu", "buick skylark 3...
#> $ Origin           <chr> "USA", "USA", "USA", "USA", "USA", "USA", "US...
#> $ Weight_in_lbs    <dbl> 3504, 3693, 3436, 3433, 3449, 4341, 4354, 431...
#> $ Year             <dttm> 1970-01-01, 1970-01-01, 1970-01-01, 1970-01-...

Chart

chart <- 
  alt$Chart(r_to_py(vega_data$cars()))$
  mark_circle()$
  encode(
    x = alt$X(alt$`repeat`("column"), type = "quantitative"),
    y = alt$Y(alt$`repeat`("row"), type = "quantitative"),
    color = "Origin:N"
  )$
  properties(width = 200, height = 200)$
  `repeat`(
      row = list("Horsepower", "Acceleration", "Miles_per_Gallon"),
      column = list("Miles_per_Gallon", "Acceleration", "Horsepower")
  )$
  interactive()

chart

Simple Scatter Plot with Labels

Altair example

Layering can now work with the + operator as it does in Python. Additionally, it can be declared as alt$layer(chart1, chart2) or as alt$ChartLayer(layer = list(chart1, chart2))

Data

Definition

data <- 
  data_frame(
    x = c(1, 3, 5, 7, 9),
    y = c(1, 3, 5, 7, 9),
    label = c("A", "B", "C", "D", "E")
  )

glimpse(data)
#> Observations: 5
#> Variables: 3
#> $ x     <dbl> 1, 3, 5, 7, 9
#> $ y     <dbl> 1, 3, 5, 7, 9
#> $ label <chr> "A", "B", "C", "D", "E"

Chart

bars <- 
  alt$Chart(r_to_py(data))$
  mark_point()$
  encode(
    x = "x:Q",
    y = "y:Q"
  )

text <-
  bars$
  mark_text(align = "left", baseline = "middle", dx = 7)$
  encode(text = "label:N")

chart <- (text + bars)

chart

Table Bubble Plot (Github Punch Card)

Altair example

Data

glimpse(vega_data$github())
#> Observations: 955
#> Variables: 2
#> $ time  <dttm> 2015-01-01 01:00:00, 2015-01-01 04:00:00, 2015-01-01 05...
#> $ count <dbl> 2, 3, 1, 1, 3, 1, 3, 1, 1, 2, 1, 3, 2, 2, 3, 1, 2, 2, 1,...

Chart

chart <- 
  alt$Chart(r_to_py(vega_data$github()))$
  mark_circle()$
  encode(
    x = alt$X("time:O", timeUnit = "hours"),
    y = alt$Y("time:O", timeUnit = "day"),
    size = "sum(count):Q"
  )

chart

Trellis Scatter Plot

Altair example

This example demonstrates the need for a ggplot2::facet_wrap()-like capability, which we understand is coming to Vega-Lite in the no-so-distant future.

Data

glimpse(vega_data$movies())
#> Observations: 3,201
#> Variables: 16
#> $ Creative_Type          <list> [NULL, NULL, NULL, NULL, "Contemporary...
#> $ Director               <list> [NULL, NULL, NULL, NULL, NULL, NULL, "...
#> $ Distributor            <list> ["Gramercy", "Strand", "Lionsgate", "F...
#> $ IMDB_Rating            <dbl> 6.1, 6.9, 6.8, NaN, 3.4, NaN, 7.7, 3.8,...
#> $ IMDB_Votes             <dbl> 1071, 207, 865, NaN, 165, NaN, 15133, 3...
#> $ MPAA_Rating            <list> ["R", "R", NULL, NULL, "R", NULL, "R",...
#> $ Major_Genre            <list> [NULL, "Drama", "Comedy", "Comedy", "D...
#> $ Production_Budget      <dbl> 8000000, 300000, 250000, 300000, 100000...
#> $ Release_Date           <list> ["12-Jun-98", "7-Aug-98", "28-Aug-98",...
#> $ Rotten_Tomatoes_Rating <dbl> NaN, NaN, NaN, 13, 62, NaN, NaN, NaN, 2...
#> $ Running_Time_min       <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,...
#> $ Source                 <list> [NULL, NULL, NULL, NULL, "Original Scr...
#> $ Title                  <list> ["The Land Girls", "First Love, Last R...
#> $ US_DVD_Sales           <dbl> NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,...
#> $ US_Gross               <dbl> 146083, 10876, 203134, 373615, 1009819,...
#> $ Worldwide_Gross        <dbl> 146083, 10876, 203134, 373615, 1087521,...

Chart

chart <- 
  alt$Chart(r_to_py(vega_data$movies()))$
  mark_point()$
  encode(
    x = "Worldwide_Gross:Q",
    y = "US_DVD_Sales:Q",
    column = "MPAA_Rating:N"
  )$
  properties(width = 200, height = 200)

chart